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Smart city article details
| Title | Multi-Target Multi-Camera Tracking Of Vehicles By Graph Auto-Encoder And Self-Supervised Camera Link Model |
|---|---|
| ID_Doc | 38433 |
| Authors | Hsu H.-M.; Wang Y.; Cai J.; Hwang J.-N. |
| Year | 2022 |
| Published | Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2022 |
| DOI | http://dx.doi.org/10.1109/WACVW54805.2022.00055 |
| Abstract | Multi-Target Multi-Camera Tracking (MTMCT) of vehicles is a challenging task in smart city related applications. The main challenge of MTMCT is how to accurately match the single-camera trajectories generated from different cameras and establish a complete global cross-camera trajectory for each target, i.e., the multi-camera trajectory matching problem. In this paper, we propose a novel framework to solve this problem using the self-supervised trajectory-based camera link model (CLM) with both appearance and topological features systematically extracted from a graph auto-encoder (GAE) network. Unlike most related works that represent the spatio-temporal relationships of multiple cameras with the laborious human-annotated CLM, we introduce a self-supervised CLM (SCLM) generation method that extracts the crucial multi-camera relationships among the vehicle trajectories passing through different cameras robustly and automatically. Moreover, we apply a GAE to encode topological information and appearance features to generate the topological embeddings. According to our experimental results, the proposed method achieves a new state-of-the-art on both CityFlow 2019 and CityFlow 2020 benchmarks with IDF1 of 77.21% and 55.56%, respectively. © 2022 IEEE. |
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